Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full disclosure burden — and meets it thoroughly. It reveals the non-equalized evaluation window (long-warmup strategies start trading later; compare actual_date_from and check result.benchmark before ranking by CAGR), the multiple-testing upward bias of reporting a winner, sequential execution with 10–50s latency, per-day quotas (Pro=20, Power=200), and the Pro-tier API requirement. This is far beyond a generic 'runs a comparison' statement.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Dense but disciplined — the opening question front-loads the purpose and each subsequent sentence adds a distinct fact: same-data guarantee, sibling differentiation, evaluation-window caveat, multiple-testing caveat, latency, quota, tier. The tail stacks several caveats and operational notes in quick succession, which is slightly heavy, but there is no redundant filler or repetition of the schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an 8-parameter tool with no output schema, no annotations, and a family of 75+ siblings, this is remarkably complete: purpose, scoping guarantees, the metric categories returned (best by CAGR, best by win-rate, worst by drawdown), behavioral caveats, latency, quota, tier, and sibling routing all appear. The high-level return shape compensates adequately for the missing output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description adds genuine meaning on top: it establishes the core guarantee that pair, interval and date range are held constant across all strategies, and warns that date_from clamping interacts with warmup so the actual data windows differ across runs. It correctly avoids restating per-parameter docs the schema already covers.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Opens with a concrete question ('Which of these strategies performed best on the same data?') and states the verb-resource pair: run 2–5 strategies against the same pair/interval/date range and return per-strategy metrics plus a comparison summary. It explicitly distinguishes itself from a series of separate arena_run_backtest calls and from arena_run_universe_backtest (one strategy across many pairs), so an agent can select it correctly without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit trigger: 'Use this when the user asks which of several strategies fits a market.' It names the alternative it competes with (a sequence of arena_run_backtest calls) and explains why this tool is superior (it holds pair/interval/date range fixed, which separate runs do not guarantee). It also routes the inverse case to arena_run_universe_backtest and the multiple-testing follow-up to arena_get_robustness_field — full when-to-use and when-not-to-use coverage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.